The Global Positioning System (GPS) is a satellite-based navigation system that provides location and time information anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites. The GPS was created by and is maintained by the U.S. government. The GPS is freely accessible to anyone with a GPS receiver. Among the many devices which make use of the GPS are aircraft.
Since 2012 (and continuing through 2025), the Federal Aviation Administration (FAA) has been undertaking a wide-ranging transformation of the air transportation system in the U.S.A., in part by implementing the Next Generation Air Transportation System (NextGen). In particular, NextGen will transform the U.S. air transportation system from being ground-based (e.g., active radar) to satellite-based (e.g., the GPS). An aspect of NextGen is that aircraft must become ‘ADS-B Out’ compliant. Anecdotal evidence suggests that at least a majority of aircraft flying in U.S. airspace is ADS-B compliant as of midway through 2015.
ADS-B (Automatic dependent surveillance-broadcast) is a cooperative surveillance technology in which an aircraft determines its position via the GPS and periodically broadcasts it, thereby facilitating the tracking of the aircraft by others, e.g., air traffic control ground stations (e.g., as a replacement for secondary radar), other aircraft (e.g., to provide situational awareness and facilitate self-separation amongst the aircraft). The term ‘ADS-B Out’ refers to the broadcasting requirements of an aircraft. ADS-B is: “automatic” in that it requires neither pilot input nor external input; and “dependent” in that it depends on data from the aircraft's navigation system, which itself is dependent (typically, to a substantial extent) upon the GPS. The payload of an ADS-B message can include: aircraft identity; aircraft location (latitude and longitude); aircraft altitude; aircraft heading; and aircraft velocity.
Some research has been conducted relating to a first concept for determining the location of a ground-based, stationary transmitter based on an aircraft's receipt of proprietary signals broadcast from (and initiated by) the stationary transmitter on the Earth's surface. In particular, the first concept is based on determining a Doppler shift in the received proprietary signals. The proprietary signals were configured to facilitate the determination of Doppler shift.
Some research has been conducted relating to a second concept for determining the location of a stationary asset positioned on the Earth' surface using ADS-B signals received by the asset. A premise informing the second concept is that an asset which receives an ADS-B signal must be located somewhere within a circle enclosing all possible locations of the asset (hereinafter, a ‘circle of possibilities’). The circle of possibilities is centered on the position of the aircraft (as identified in the ADS-B signal) and has as assumed maximum radius. As successive ADS-B signals are received from the aircraft, the corresponding successive circles of possibilities form a swath of possibilities on a geographic map. The centerline of the swatch follows the trajectory of the aircraft. If a few swaths of possibilities are received from a few aircraft having substantially diverse trajectories, then the intersections of the respective swaths yield a refined circle of possibilities. The second technique was relatively inaccurate; with the refined circle of possibilities having a radius of about two miles.
Some research has been proposed relating to a third concept for determining the location of a mobile asset positioned on the Earth' surface using ADS-B signals received by the asset. The third concept refines the second concept by attaching a weighting factor to the received ADS-B signals. In particular, the weighting factor is a particular metric, namely the received signal strength indicator (RSSI), of the ADS-B signal as received by the asset. It is noted that RSSI is a measurement of the power present in a received radio signal, e.g., an instance of an ADS-B signal.
Some research has proposed relating to a fourth concept for determining the location of a mobile asset positioned on the Earth' surface using ADS-B signals received by the asset. In particular, the fourth concept is based on determining a Doppler shift in the received ADS-B signals.
In general, it is known that Bayes filters can probabilistically estimate a dynamic system's state from noisy observations. In location estimation for the Internet of Things (IoT), the state is an object's location, and location sensors provide observations about the state. The state could be a simple 2D position or a complex vector including 3D position, pitch, roll, yaw, and linear and rotational velocities. Bayes filters represent the state at time t by random variables xt. At each point in time, a probability distribution over xt, called belief, Bel(xt), represents the uncertainty. Bayes filters aim to sequentially estimate such beliefs over the state space conditioned on all information contained in the sensor data.
Kalman filters are a widely used variant of Bayes filters. A Kalman filter is two-step process (a prediction step followed by an update step) that is applied iteratively. In the prediction step, the Kalman filter uses a model to produce estimates of the current state variables, along with their uncertainties. Once the outcome of the next measurement (necessarily corrupted with some amount of error, including random noise) is observed, the estimates (from the prediction step) are updated (in the update step) using a weighted average, with more weight being given to estimates with higher certainty. The algorithm is recursive. An initial prediction by the Kalman filter is based only on initial measurements. Thereafter, the Kalman filter needs only present input measurements, the previously calculated state per se and its corresponding uncertainty matrix; no additional past information is needed.